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arXiv · 2407.03380

Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties

Srivathsan Badrinarayanan·Chakradhar Guntuboina·Parisa Mollaei·Amir Barati Farimani
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Abstract

Peptides are essential in biological processes and therapeutics. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with Graph Neural Networks (GNNs) to predict peptide properties. We combine PeptideBERT, a transformer model tailored for peptide property prediction, with a GNN encoder to capture both sequence-based and structural features. By employing Contrastive Language-Image Pre-training (CLIP), Multi-Peptide aligns embeddings from both modalities into a shared latent space, thereby enhancing the model's predictive accuracy. Evaluations on hemolysis and nonfouling datasets demonstrate Multi-Peptide's robustness, achieving state-of-the-art 86.185% accuracy in hemolysis prediction. This study highlights the potential of multimodal learning in bioinformatics, paving the way for accurate and reliable predictions in peptide-based research and applications.

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BibTeXRIS

Srivathsan Badrinarayanan, Chakradhar Guntuboina, Parisa Mollaei, Amir Barati Farimani. 2024-07-02. Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties. https://arxiv.org/abs/2407.03380

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